Solving the three-dimensional (3D) structure of biological macromolecules using NMR spectroscopy has been useful for the development of several currently used therapeutic drugs and will likely play a key role in the development of many future drugs. Structure determination by this approach is essentially a 3-step process requiring resonance assignment (determining which nucleus in the molecule gives rise to which NMR signal), derivation of structural restraints from NMR parameters (such as interatomic distance and torsion angle constraints) and structural modeling of the data. It is known that the precision and usefulness of the final structure increases with the number of restraints used in the structure calculation. As both human analysis time and spectrometer time are limiting resources, it is critical to maximize the efficiency of data collection and analysis for both resonance assignment and restraint determination. The use of empirically determined correlations between NMR observables and structural parameters has been vital to the progress of NMR structure determination and newly discovered correlations are certainly expected to generate similar advances. NMR and x-ray structures are currently being determined at a rate of approximately 5,000 per year. The Protein Databank (PDB) now houses the coordinates of over 27,000 structures and the BioMagResBank (BMRB) contains NMR parameters for over 3,000 macromolecules. These databanks provide an enormous resource for discovering novel correlations between the structural features of molecules and the NMR parameters which reflect them. While the architectures of the PDB and BRMB are extraordinary for the archival and retrieval of this data to the global scientific community, the public databanks do not offer the capability for mining this enormity of data for correlations useful to NMR structure determination. This capability is essential in order to fully exploit the vast amounts of available postgenomic data. We propose to develop such a resource and use it to mine for important correlations hidden within the data which are useful in improving the efficiency and effectiveness of NMR analysis. [unreadable] [unreadable] [unreadable] [unreadable]